Single Vehicle Research Articles

アイドリング振動とエンジンシェイクを考慮した簡易モデルを用いたエネルギー伝達特性による分析

In automotive development, it is important to balance various phenomena with a simplified vehicle body model in the initial design phase. Additionally, idling vibration, engine shake, and road noise are phenomena that affect vehicle ride comfort. Since they have different exciting components and target frequencies, each simplified vehicle model examined in the past report is different. Therefore, it is necessary to represent of each phenomenon in a single vehicle model. Therefore, in recent years, much research has been done in initial design techniques called Model Based Development (MBD) and 1D CAE. We have studied using the Statistical Energy Analysis (SEA) for the initial design. Especially an energy transmissibility parameter of SEA which is called by coupling loss factor (CLF) is focused on describing the phenomenon for various kinds of vibration and noise problem. In this paper, a fundamental study is conducted to examine the balance of multiple phenomena in the initial design phase by the energy transmissibility. At first, a simplified vehicle model that represents each phenomenon is constructed. Next, the energy transmissibility is used to represent each phenomenon, and vibration reduction guidelines are obtained and verified through balance design. The obtained guidelines are then applied numerically to the actual vehicle to confirm vibration reduction.

Dual-color core-shell silica nanosystems for advanced super-resolution biomedical imaging.

Fluorescent core-shell silica nanoparticles are largely employed in nanomedicine and life science thanks to the many advantages they offer. Among these, the enhancement of the stability of the fluorescent signal upon fluorophore encapsulation into the silica matrix and the possibility to combine in a single vehicle multiple functionalities, physically separated in different compartments. In this work, we present a new approach to the Stöber method as a two-cycle protocol for the tailored synthesis of dual-color fluorescent core-shell silicon dioxide nanoparticles (SiO2 NPs) using two commercial dyes as model. To facilitate the colloidal stability, the nanoparticle surface was functionalized with biotin by two approaches. The biotinylated nanosystems were characterized by several analytical and advanced microscopy techniques including Fourier transform infrared (FT-IR) spectroscopy, dynamic light scattering (DLS), UV-vis, transmission electron microscopy (TEM) and confocal laser scanning microscopy (CLSM). Moreover, advanced super-resolution based on structured illumination was used for the imaging of the double-fluorescent NPs, both on a substrate and in the cellular microenvironment, at nanometric resolution 100 nm, in view of their versatile potential employment in fluorescence optical nanoscopy as nanoscale calibration tools as well as in biomedical applications as biocompatible nanosystems for intracellular biosensing with high flexibility of use, being these nanoplatforms adaptable to the encapsulation of any couple of dyes with the desired function.

Про визначення вантажопідйомності автодорожніх мостів

Information is provided on the methodological recommendations developed and adopted by Ukravtodor for determining the carrying capacity of road bridges. The main provisions of these recommendations are briefly outlined. For practical application, the carrying capacity of the bridge is defined as: – the maximum total mass of the vehicle moving in the column is permissible; – the maximum total mass of a heavy vehicle moving in a single order is permissible; – permissible maximum load on the axle of the vehicle. The initial data for determining the actual carrying capacity of the bridge are the results of its inspection and data from technical documentation. All detected defects and deviations of the structure from the project should be analyzed and taken into account when determining its actual carrying capacity. The carrying capacity of the bridge is defined as the minimum value, which was determined by checking the boundary states for its main structures: supports, span structures and slabs of the carriageway. Inspections of sections of structures are performed by comparing the calculated force from the time load and the limiting force for the cross-section of the structure to be checked, minus the forces from constant and other loads. Recommendations for the selection of sections that are checked and algorithms for these checks are provided. When determining the carrying capacity in the form of an acceptable total mass of the vehicle in the column, the temporary load is taken according to the load schemes H-30 and H-40. These loads are truck columns weighing 30 tons each and weighing 40 tons each. The carrying capacity for the permissible weight of a single heavy vehicle is determined according to the NK-80 scheme (weight parameter 80 tons). The carrying capacity for the permissible axle load is determined by multiplying the load capacity ratio relative to the load according to the A-11 scheme (weight parameter 12 tons). Road signs in front of the bridge, which limit the weight parameters of vehicles, must have the value of the actual carrying capacity of the bridge according to the permissible total mass of the vehicle in the column and the permissible axle load, respectively. Keywords: road bridge, carrying capacity, road signs, technical condition.

Comparative Analysis of Single, Double and Quad Electric Vehicle Powertrain Systems

Electric vehicles offer a promising solution to solving environmental problems related to air pollution. The typical powertrain design of an electric vehicle includes a relatively high-speed-low-torque electric motor integrated with some mechanical system to transmit the output mechanical energy to the wheels, such as the gearbox, transmission and differential. Different drivetrain configurations with single, double and quad electric motors can be used in electric vehicles. In this study, it is aimed to compare the effects of different powertrain configurations on the acceleration performance, range and energy consumption of the electric vehicle. Single, dual and quad motor electric vehicle powertrain systems were simulated with MATLAB Simulink according to the New European Driving Cycle (NEDC). The Volvo XC40 Recharge electric car was used as the reference vehicle with the same vehicle characteristics, battery type/capacity and the same total power output by changing only the electric motor powertrain configurations. It has been determined that quadruple electric motor powertrain configuration has lower energy consumption, longer range and better acceleration performance than the single and double electric motor powertrain configurations.

Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery

In digital era, being stranded from very basic telecommunication protocols and internet makes vehicle rerouting-like crucial tools more difficult or even impossible, especially in times of disaster and emergency. In this study, we propose a modular rerouting framework for only one single vehicle composed of visual perception, property estimation and trajectory optimization, which enables to generate optimum paths exploiting aerial imagery. Once the deep network, which is fine-tuned on newly introduced dataset herein named MaVefAI, processes the input, the following module estimates pose, motion direction and speed of detected vehicles. Afterwards, we link the appropriate vehicles via graphs to obtain group properties that pave the way for estimating the traffic congestion level. In the end, we get the output as the optimum path from the independent trajectory optimization module to which required inputs are already sent by preceding modules. We solve the multi-objective cost function subject to velocity and congestion intervals, which comprises distance, traffic congestion level, and angle inconsistency. We employ Dijkstra, A*, RRT, and RRT* to optimize the cost while vast majority of existing works focus to optimize via single method. The fine-tuned segmentation model accuracy becomes more than 98% for vehicle groups thanks to MaVefAI. The extensive experiments reveal that all algorithms follow the same path. However, RRT* achieves the fastest result by examining most of the possible options in less time, which also appears to be the most robust method comparing with the alternatives for route optimization. Our dataset MaVefAI is publicly available here: https://precisioncomputing.sakarya.edu.tr.

Bridge vehicle-induced effect influence line characteristic function based on monitoring big data: definition and identification

In this paper, an extended influence line parameter under the action of statistical steady-state traffic flow loads is defined, which is called the vehicle-induced effect influence line characteristic function (ILF). It is theoretically proved that this function can characterize the static characteristics of bridge structures, and can also be used to track and monitor the evolution of static characteristics of bridge structures throughout their service periods. On this basis, this paper presents a set of methods to identify this function from monitoring big data, mainly based on extracting the static effect of a single vehicle on the structure, including methods such as empirical mode decomposition (EMD), spectrum analysis, and mode reorganization. The set of methods only requires monitoring structural effects, without the need for traffic load information, which greatly reduces the requirements for the monitoring system, with simple implementation and high operability. Through the application of the monitoring data of the actual bridge, the results show that the recognition effect of the vehicle-induced strain influence line characteristic function () by the proposed method is in line with the theoretical expectation, and it is suitable for the actual complex and changeable monitoring environment with good robustness. The identification technology of ILF proposed in this paper can be further used to study a new index system to characterize the static characteristics of the structure, thus opening up a new methodological field for the intelligent perception and diagnosis of the structure under the condition of monitoring big data.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

АНАЛИЗ ВЛИЯНИЯ РЕЖИМОВ ДВИЖЕНИЯ ЛЕСОВОЗНОГО ТЯГАЧА С ПОЛУПРИЦЕПОМ НА НАГРУЖЕННОСТЬ СЕДЕЛЬНО-СЦЕПНОГО УСТРОЙСТВА

The article presents the advantages of timber tractors with semi-trailers in comparison with single timber vehicles. The existing road condi-tions for the operation of saddle road trains in-volved in the transportation of timber to con-sumers are described. The causes and conse-quences of loading elements of saddle-coupling devices of road trains are considered. The de-pendences of the change in forces in the cou-pling device on the values of the coefficients of adhesion and the distribution of braking forces, the deceleration of the saddle road train, and also on the height of the center of gravity of the semi-trailer of the road train are presented. The regularities of changing the braking distance, deceleration and compression force in the fifth wheel coupling device of a road train due to un-derutilization of the coupling weight of a tractor with a semi-trailer are considered. The depend-ences of changes in loads in the fifth wheel cou-pling on road conditions, the load of the road train, the location of the fifth wheel coupling on the tractor, as well as on the damping coefficient are analyzed. An analysis of oscillograms of changes in forces in the pivot of the device in the process of sharp braking on a curvilinear and rectilinear trajectory with various options for braking by individual links is given. The influ-ence of the road train load, the speed of its movement, the type of road surface, the design parameters of the coupling device, the presence of lubricant on the base plate, the level of its wear on the change in the moment of resistance in such a device are described. The necessity of increasing the efficiency of timber tractors with semi-trailers by improving the existing designs of fifth wheel couplings is substantiated.

Differences in frequency of occurrence, event characteristics, and pre-impact vehicle kinematics between crashes, near-crashes, and single vehicle conflicts in a large-scale naturalistic driving study

Objective: Motor vehicle crashes result in egregious personal injury, mortality, and economic cost but are relatively rare in naturalistic observations. There is, however, evidence of strong relationships between crashes and less severe (but more common) “surrogate” events (e.g., near-crashes). Despite this strong relationship, there can still be some important differences in findings when these surrogate events are investigated in lieu of, or combined with, crashes. Therefore, it is relevant to describe and quantify differences between crashes and crash-surrogate events. Consequently, the focus of this investigation was to establish how crashes and crash surrogate events in a large-scale naturalistic driving study compare in terms of frequency of occurrence, event characteristics, and pre-impact vehicle kinematics. Methods: Crashes, near-crashes, and single-vehicle conflicts (SVCs) derived from the Second Strategic Highway Research Program Naturalistic Driving Study were coded to summarize the environmental and contributing variables involved. The original coding for these events was downsized to the variables of interest, and those variables underwent recoding to simplify the coded options. Additional variables based on the kinematic characteristics for each event were also derived and analyzed. Multinomial logistic regression was used to assess the contributions of these different variables toward classification of an event as a crash, near-crash, or SVC. Results: The regression model comparing crashes with near-crashes and SVCs identified several variables that allowed differentiation between crashes and these surrogates, primarily the pre-incident maneuver of the subject vehicle and the evasive maneuver that was executed by the driver. Kinematic variables prior to event onset, however, were not predictive of event outcome. Conclusions: The results suggest that important differences exist between crashes and their near-crash surrogates, and between crashes and SVCs. These results, however, should not discourage the analysis of surrogate events, which still provide useful information in prevention and mitigation of crash circumstances. This investigation highlights how crashes are different from two types of surrogate events and provides information that may allow for more precise analysis of these surrogate events in the future.

Development and evaluation of a human machine interface to support mode awareness in different automated driving modes

With increasing implementation of automated driving technology it is expected that different automation modes will be present within the same vehicle and within a single trip. At all times during automated driving the driver needs to have ‘mode awareness’, which is an understanding of the automation mode and the corresponding responsibilities. Yet, research on HMI design to support mode awareness for multiple automation modes within a single vehicle and within a single trip is currently limited. The current work describes the development and evaluation of a Human Machine Interface (HMI) to support mode awareness while driving in different automation modes. The work exists of three phases: Phase 1 defines functional requirements for HMI design based on literature review and 5 experimental studies including 146 participants. Phase 2 implements the functional requirements in HMI design through expert and focus group sessions. Phase 3 evaluates and improves upon the HMI design employing virtual reality and the RITE (Rapid Iterative Testing and Evaluation) method with 18 participants. The result is a continuous and holistic HMI design creating mode awareness through ambience. Findings from Phase 3 and previous research indicate that this HMI is comprehended well, with a relatively low task load, and with a good experienced system usability. It is important to additionally evaluate the HMI design resulting from the current study in driving simulators and in on-road tests. Such tests will provide an opportunity to verify and expand on the current study’s findings and to contribute to guidelines for HMI design.

Energy-Efficient Cooperative Offloading for Edge Computing-Enabled Vehicular Networks

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

A Survey on Attack Detection and Resilience for Connected and Automated Vehicles: From Vehicle Dynamics and Control Perspective

Recent advances in attack/anomaly detection and resilience strategies for connected and automated vehicles (CAVs) are reviewed from vehicle dynamics and control perspective. Compared to traditional vehicles, CAVs are featured in the increasing number of perception sensors, advanced intra-vehicle communication technologies, capabilities of driving automation and connectivity between single vehicles. These features bring about safety issues which are not encountered in traditional vehicle systems. One main type of these issues is the attack or anomaly launched onto the perception sensors and the communication channels. With such a consideration, this survey summarizes and reviews the existing results on attack/anomaly detection and resilience of CAVs in control frameworks. This paper reviews the results according to the positions at which the attacks/anomalies occur. These positions are divided into three categories, namely, intra-vehicle communication network, perception sensors and inter-vehicle communication network. From this perspective, the recent attack/anomaly detection and resilience results are reviewed according to different positions attacked. After reviewing existing results, some potential research directions and challenges are identified.

Effects of air pollution due to vehicular emission in coimbatore and reduction strategies: A review

Air pollution is a Worldwide challenge that is a reason for premature death. It affects both developing nations and developed nations. In particular, the air is so much polluted so that cities are fighting to fulfill air quality range and protect humans from harmful substances. One of the greatest global challenges of 2050 is the reduction of greenhouse gas emissions. Besides greenhouse gas emissions, air pollution is the major problems in many urban areas this situation is due to the sudden increase in the count of vehicles. According to the studies, Nitric oxide and particulate matter contribute more to air pollution.Due to urban transportation improvements vehicle traffic volume is on the rise over the past few years. Air pollution is majorly contributed by vehicular congestion and traffic. This paper analyzes traffic data at Coimbatore. From the review, comparing various locations, two-wheelers are the highest in count among all of the rest of the vehicles. Air pollution is a crisis that causes damage to the human who are living in areas where air pollutants are high in level. The type and amount of air pollutant decides the risk of illness. This indicates that carbon monoxide and particulate matter emitted from a vehicle of two passengers is higher than a single passenger vehicle. Although the centralization of poisons close by avenue are not always connected to transportation sector, this marker can be used as an indication of increase in residents. It also acts as an indication of how much surrounding areas are being polluted by metropolitan toxic wastes. This paper examines the vehicular emissions in Coimbatore, India. The study looks at reviewing data from various sources to formulate solutions for improving air quality in the city.

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem.

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in which the fleet is assumed as repetitions of a single vehicle. Hence, their key to construct a solution solely lies in the selection of the next node (customer) to visit excluding the selection of vehicle. However, vehicles in real-world scenarios are likely to be heterogeneous with different characteristics that affect their capacity (or travel speed), rendering existing DRL methods less effective. In this article, we tackle heterogeneous CVRP (HCVRP), where vehicles are mainly characterized by different capacities. We consider both min-max and min-sum objectives for HCVRP, which aim to minimize the longest or total travel time of the vehicle(s) in the fleet. To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step. Experimental results based on randomly generated instances show that, with desirable generalization to various problem sizes, our method outperforms the state-of-the-art DRL method and most of the conventional heuristics, and also delivers competitive performance against the state-of-the-art heuristic method, that is, slack induction by string removal. In addition, the results of extended experiments demonstrate that our method is also able to solve CVRPLib instances with satisfactory performance.

Feedback Control for Collision-Free Nonholonomic Vehicle Navigation on SE(2) With Null Space Circumvention

In this article, we present a novel feedback controller for the collision-free navigation of a class of nonholonomic vehicles based on navigation functions in convex environment. This controller utilizes a high-level principle— <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">null space circumvention</i> —in nonholonomic navigation control, allowing us to involve different vehicle kinematics and navigation functions with respect to the task and environment. For a single vehicle, we have proved that the controller can steer it to the target on SE(2) while avoiding collision from almost all the initial states. The controller can also close the gap between holonomic vehicle navigation and nonholonomic vehicle navigation, allowing the navigation functions created for holonomic vehicles to be used for nonholonomic vehicles. Furthermore, we extend the controller to multivehicle formation control. Within this task, our controller shows generality without much modification except for updating the gradient of the chosen navigation functions. Simulation and experimental results demonstrate that the proposed controllers can achieve collision-free navigation for a single vehicle and multiple vehicles.

Driver Fatigue Level Prediction

Public transport networks (PTNs) are difficult to use when the user is unfamiliar with the area they are traveling to. This is true for both infrequent users (including visitors) and regular users who need to travel to areas with which they are not acquainted. In these situations, adequate on-trip navigation information can substantially ease the use of public transportation and be the driving factor in motivating travellers to prefer it over other modes of transportation. However, estimating the localization of a user is not trivial, although it is critical for providing relevant information. I assess relevant design issues for a modular cost-efficient user-friendly on-trip Navigation service that uses position sensors. By helping travellers move from single occupancy vehicles to public transportation systems, communities can reduce traffic congestion as well as its environmental impact. Here, I describe our efforts to increase the satisfaction of current public transportation users and help motivate more people to ride. I can help existing riders and encourage new riders by enhancing the usability of public transportation through good transit traveller information systems. The motivation for every location-based information system is: “To assist with the exact information, at right place in real time with personalized setup and location sensitiveness”. LOCATION-BASED services are increasingly important for modern mobile devices such as the Smartphone. An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays due to positioning and communication, and changing positioning accuracy. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking, are different from those in the normal state. In this project, we propose a system called DriCare, which detects the drivers’ fatigue status, such as blinking, and duration of eye closure, using video images, without equipping their bodies with devices. Owing to the shortcomings of previous algorithms, we introduce a new face-tracking algorithm to improve the tracking accuracy. Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers’ state. By combining the features of the eyes and mouth, DriCare can alert the driver using a fatigue warning.

Dynamic size optimization approach to support railway carbody lightweight design process

The transition to a globalised electrical power supply pushes rolling stock manufacturers to find structural solutions for reducing the power required by the vehicles. Within a market where details can make the difference in economic terms, the concept of design optimization is becoming established. However, current industrial procedure for the evaluation of carbody structural strength, in static field and according to EN 12663-1:2015 standard, does not include any optimization process. More in general, optimization processes, and especially dynamic optimization, are not widely used for designing of railway vehicles. In this framework, the present paper proposes a new dynamic optimization approach to support the design of railway vehicle carbodies subjected to static loads. Proposed methodology aims to minimize the mass of the metallic structure, working on the thicknesses of the optimized components, maintaining the baseline geometry. The constraint function was imposed on the first natural vibration frequency of the system. The optimization strategy is based on a dynamic size optimization process in conjunction with modal analysis techniques, applied on the single carbody shell. The procedure has involved the roof assembly of a single tram vehicle body. The proposed approach is resulted numerically efficient in terms of calculation times. Encouraging results have been achieved in terms of mass saving and mechanical behaviour of the carbody shell. Optimized components were 21% lighter than the original, that corresponds to 3% if evaluated on the total mass of the single carbody metallic structure. The methodology turns out to be a useful tool for supporting designers to reduce the mass of the carbody structures, reducing overdimensioning condition that could affect the carbody structure.

Analysis on Lane Capacity for Expressway Toll Station Using Toll Data

Toll stations are bottlenecks in the traffic flow of expressways, and the evaluation of their capacity is essential for the operation of the expressway. Traditional capacity studies are mostly based on theoretical modelling of traffic engineering or simulation, with a focus on parameter tuning and idealized hypotheses, resulting in poor reliability. In view of the coexistence of electronic toll collection lanes and compound toll collection lanes at toll stations of expressways in China, the present study analyses the capacity of entrance and exit lanes of toll stations under mixed traffic conditions using a real toll data-driven approach. Firstly, the service time of a single vehicle during the saturation period was taken as the starting point for the capacity estimates. Secondly, the variation in service time for multiple categories of vehicles is modelled using lognormal distribution. Finally, the capacity of the two types of toll lanes at the designated toll station is determined. The important outcome of this study is the calculation of authentic capacity at the toll stations and the discussion of individual special toll lanes. Accordingly, it contributes to the development of appropriate policies to manage the operation of the toll plaza effectively.

The Menin Inhibitor Ziftomenib (KO-539) Synergizes with Agents Targeting Chromatin Regulation or Apoptosis and Sensitizes AML with MLL Rearrangement or NPM1 Mutation to Venetoclax

MLL1-rearranged (MLL-r) leukemias are dependent on the interaction of the adaptor protein menin with the oncogenic MLL (KMT2A) fusion protein. We previously demonstrated that NPM1 mutant (NPM1mut) acute myeloid leukemias (AMLs) exhibit a similar dependency on the menin interaction with wildtype MLL, a histone 3 lysine 4 methyltransferase. Pharmacologic inhibition of the menin-MLL interaction reverses MEIS1/HOX-driven leukemic gene expression, induces differentiation, and has anti-leukemic activity in preclinical NPM1mut and MLL-r AML models. Ziftomenib is currently assessed in clinical phase I/II trials and explorative efficacy against relapsed / refractory AML has been demonstrated. Here, we characterized the effects of ziftomenib on MLL-r and NPM1mut AML in detail and systematically screened for synergistic drug combination partners. First, we assessed the single-agent effects of ziftomenib on MLL-r (MOLM13, MV411, OCI-AML2) and NPM1mut (OCI-AML3) AML cell lines. Cellular assays demonstrated profound dose-dependent killing after seven days of treatment with IC50 values<25nM. HL60 and NB4 cells that are both wildtype for NPM1 and MLL served as negative controls and were not affected. Similar to previously characterized menin inhibitors (menin-i), ziftomenib uniformly induced transcriptional downregulation of MEIS1, PBX3, FLT3, and BCL2 in these leukemias, while genes upregulated upon ziftomenib were enriched for myelomonocytic differentiation processes. Ziftomenib-induced differentiation was also evidenced by cytology and upregulation of cell surface CD14 and CD11b. To identify synergistic combinations, we designed a screen assessing single and combined effects of ziftomenib and 37 targeted compounds (selected by confirmed preclinical or clinical activity against AML), each at four different concentrations and in a constant 1:4 ratio. We observed significant synergistic effects (combination index<0.5) of ziftomenib in MLL-r and NPM1mut AML, particularly pronounced for agents targeting chromatin regulation & DNA damage (e.g., LSD1, PRMT5, and PARP) as well as apoptosis & cell cycle (e.g. BCL2, MCL1, and CDK4/6). As we have demonstrated with other menin-i, ziftomenib induced transcriptional downregulation of FLT3 in the FLT3-mutant AML setting and also exhibited synergistic activity with the FLT3 inhibitor gilteritinib. As ziftomenib also uniformly downregulated BCL2, we assessed its combination with venetoclax in more detailed response assays and found synergistic growth inhibition compared to single treatment or vehicle control in all tested MLL-r and NPM1mut AMLs. While OCI-AML3 cells are not sensitive to venetoclax, the combination with ziftomenib was synergistic when using prolonged combined agent exposure (5 days) but not with standard 24-48h venetoclax treatment. shRNA-based knockdown of MEN1 (menin) also sensitized MOLM13 to venetoclax. Next, we found by Annexin V staining that the combination enhanced apoptosis compared to single agent treatment. Also, BCL2 homology (BH) 3 profiling revealed that ziftomenib treated MLL-r and NPM1mut AML cells exhibited a significantly enhanced cytochrome c release in response to BIM, PUMA, BAD, and MS1 peptides. These findings suggest that ziftomenib-induced apoptotic priming may contribute to synergistic effects with venetoclax in these leukemias. Next, we assessed the combination in a human MV411-derived leukemic xenograft model in vivo. We found significantly reduced leukemia burden defined by bone marrow engraftment after 14 days of treatment compared to the single agent or vehicle control animals. Single agent and combination assessment of four primary human NPM1mut AML patient samples in a human stroma cell co-culture model also showed the most pronounced anti-leukemic effects for the ziftomenib-venetoclax combination compared to all other controls. In summary, we demonstrated that ziftomenib has significant activity against MLL-r and NPM1mut AML and synergizes with certain agents targeting chromatin regulation and apoptosis. The combination of ziftomenib with venetoclax is particularly synergistic and already available for clinical trial assessment.

Applying the Geodetic Adjustment Method for Positioning in Relation to the Swarm Leader of Underwater Vehicles Based on Course, Speed, and Distance Measurements

The research consisted of simulating the movement of a single vehicle in relation to the swarm leader on a square-shaped path, taking into account measurement errors characteristic of typical cheap navigation devices and the hydroacoustic system. The research showed that these methods allow for estimating position coordinates with an accuracy of about 0.5 m (RMS) in the case of a calibrated navigation system and about 3.6 m (RMS) in the case of a non-calibrated navigation system. It also showed that it can provide a higher accuracy of estimating position coordinates in terms of abeam angles of the swarm leader (relative bearing equal to approximately ±90°), as well as while ensuring minimizing systematic errors values and proper estimation of mean errors values concerning course and speed measurements.